Multiple neurocognitive systems contribute simultaneously to human decision making and learning. For example, dopaminergic projections to striatum contribute to value-based decision making by implementing a form of model-free reinforcement learning. Prefrontal cortex executive functions contribute more general and complex computations, such as actively maintaining single trial information in working memory. How the systems work together is not well understood. Here, we investigate thow their contributions to learning change during adolescence. We predicted separable effects of development on each system, and that behaviors dependent on striatal function would mature earlier than those dependent on prefrontal function. We tested 160 youth (ages 8-17 years) and 53 adults in four different reinforcement learning tasks, including RLWM (Collins&Frank), a task designed to separate out contributions of working memory from reinforcement learning. We used computational model fitting to identify individual markers of working memory (such as capacity) and reinforcement learning (such as learning rate). Contrary to our prediction, we found no effect of age on the working memory parameters. However, we found strong effects of age on reinforcement learning, with increasing learning rates and decreasing neglect of negative feedback across adolescence. These results shed new light on the developmental science of learning in adolescence.